SOTAVerified

Multi-class Classification

Multi-class classification is a type of supervised learning where the goal is to assign an input to one of three or more distinct classes. Unlike binary classification (which has only two classes), multi-class classification handles multiple labels and uses algorithms like logistic regression, decision trees, random forests, SVMs, or neural networks to predict the correct category based on the features of the input data.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 111–120 of 903 papers

TitleStatusHype
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms—0
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification—0
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference—0
Automated Multi-Label Classification based on ML-Plan—0
Automatic Classification of Functional Gait Disorders—0
Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks—0
Automatic Identification and Classification of Bragging in Social Media—0
A Universal Growth Rate for Learning with Smooth Surrogate Losses—0
Show:102550
← PrevPage 12 of 91Next →
All datasetsCOVID-19 CXR DatasetCOVID chest X-rayReuters-52TII-SSRC-23Training and validation dataset of capsule vision 2024 challenge.

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COVID-CXNetAccuracy (%)94.2—Unverified
#ModelMetricClaimedVerifiedStatus
1COVID-ResNetF1 score0.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SVM (tficf)Macro F173.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Extra TreesF1-Score93.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Model EnsembleMean AUC0.99—Unverified